edu.cmu.tetradapp.model.AbstractAlgorithmRunner Maven / Gradle / Ivy
///////////////////////////////////////////////////////////////////////////////
// For information as to what this class does, see the Javadoc, below. //
// Copyright (C) 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, //
// 2007, 2008, 2009, 2010, 2014, 2015, 2022 by Peter Spirtes, Richard //
// Scheines, Joseph Ramsey, and Clark Glymour. //
// //
// This program is free software; you can redistribute it and/or modify //
// it under the terms of the GNU General Public License as published by //
// the Free Software Foundation; either version 2 of the License, or //
// (at your option) any later version. //
// //
// This program is distributed in the hope that it will be useful, //
// but WITHOUT ANY WARRANTY; without even the implied warranty of //
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the //
// GNU General Public License for more details. //
// //
// You should have received a copy of the GNU General Public License //
// along with this program; if not, write to the Free Software //
// Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA //
///////////////////////////////////////////////////////////////////////////////
package edu.cmu.tetradapp.model;
import edu.cmu.tetrad.data.*;
import edu.cmu.tetrad.graph.EdgeListGraph;
import edu.cmu.tetrad.graph.Graph;
import edu.cmu.tetrad.graph.Node;
import edu.cmu.tetrad.graph.NodeType;
import edu.cmu.tetrad.search.ImpliedOrientation;
import edu.cmu.tetrad.session.ParamsResettable;
import edu.cmu.tetrad.util.Parameters;
import edu.cmu.tetrad.util.Unmarshallable;
import java.io.IOException;
import java.io.ObjectInputStream;
import java.util.*;
/**
* Implements a stub that basic algorithm wrappers can extend if they take
* either a dataModel model or a workbench model as parent. Contains basic
* methods for executing algorithm and returning results.
*
* @author Joseph Ramsey
*/
public abstract class AbstractAlgorithmRunner
implements AlgorithmRunner, ParamsResettable, Unmarshallable {
static final long serialVersionUID = 23L;
private DataWrapper dataWrapper;
/**
* @serial Can be null.
*/
private String name;
/**
* The parameters guiding this search (when executed).
*
* @serial Cannot be null.
*/
private Parameters params;
/**
* Keeps a reference to the dataModel source that has been provided
* (hopefully either a dataModel model or a graph).
*
* @serial Can be null.
*/
private transient DataModel dataModel;
/**
* Keeps a reference to the source graph, if there is one.
*
* @serial Can be null.
*/
private Graph sourceGraph;
/**
* Keeps a reference to the result graph for the algorithm.
*
* @serial Can be null.
*/
private Graph resultGraph = new EdgeListGraph();
/**
* The initial graph for the algorithm, if feasible.
*/
private Graph externalGraph;
/**
* A series of graphs that the search algorithm might search over, if
* it's that kind of algorithm.
*/
private List graphs;
private Map allParamSettings;
final Map paramSettings = new LinkedHashMap<>();
//===========================CONSTRUCTORS===========================//
/**
* Constructs a wrapper for the given DataWrapper. The DatWrapper must
* contain a DataSet that is either a DataSet or a DataSet or a DataList
* containing either a DataSet or a DataSet as its selected model.
*/
public AbstractAlgorithmRunner(DataWrapper dataWrapper,
Parameters params, KnowledgeBoxModel knowledgeBoxModel) {
if (dataWrapper == null) {
throw new NullPointerException();
}
if (params == null) {
throw new NullPointerException();
}
this.params = params;
this.sourceGraph = dataWrapper.getSourceGraph();
DataModelList dataSource = dataWrapper.getDataModelList();
this.dataWrapper = dataWrapper;
//temporary workaround to get the knowledge box to coexist with the dataWrapper's knowledge
if (knowledgeBoxModel == null) {
getParams().set("knowledge", dataWrapper.getKnowledge());
} else {
getParams().set("knowledge", knowledgeBoxModel.getKnowledge());
}
List names = dataSource.getVariableNames();
transferVarNamesToParams(names);
}
/**
* Constructs a wrapper for the given DataWrapper. The DatWrapper must
* contain a DataSet that is either a DataSet or a DataSet or a DataList
* containing either a DataSet or a DataSet as its selected model.
*/
public AbstractAlgorithmRunner(DataWrapper dataWrapper,
Parameters params, KnowledgeBoxModel knowledgeBoxModel, IndependenceFactsModel facts) {
if (dataWrapper == null) {
throw new NullPointerException();
}
if (params == null) {
throw new NullPointerException();
}
this.params = params;
this.sourceGraph = dataWrapper.getSourceGraph();
DataModel dataSource = getSelectedDataModel(dataWrapper);
this.dataWrapper = dataWrapper;
//temporary workaround to get the knowledge box to coexist with the dataWrapper's knowledge
if (knowledgeBoxModel == null) {
getParams().set("knowledge", dataWrapper.getKnowledge());
} else {
getParams().set("knowledge", knowledgeBoxModel.getKnowledge());
}
getParams().set("independenceFacts", facts.getFacts());
List names = dataSource.getVariableNames();
transferVarNamesToParams(names);
}
public AbstractAlgorithmRunner(DataWrapper dataWrapper, Parameters params) {
if (dataWrapper == null) {
throw new NullPointerException();
}
if (params == null) {
throw new NullPointerException();
}
this.params = params;
this.sourceGraph = dataWrapper.getSourceGraph();
DataModel dataSource = getSelectedDataModel(dataWrapper);
this.dataWrapper = dataWrapper;
List names = dataSource.getVariableNames();
transferVarNamesToParams(names);
}
/**
* Constructs a wrapper for the given graph.
*/
public AbstractAlgorithmRunner(Graph sourceGraph, Parameters params) {
if (sourceGraph == null) {
throw new NullPointerException(
"Source graph must not be null.");
}
if (params == null) {
throw new NullPointerException("Parameters must not be null.");
}
this.params = params;
List names = measuredNames(sourceGraph);
transferVarNamesToParams(names);
this.sourceGraph = sourceGraph;
}
public AbstractAlgorithmRunner(Graph graph, Parameters params,
KnowledgeBoxModel knowledgeBoxModel) {
this(graph, params);
if (knowledgeBoxModel != null) {
getParams().set("knowledge", knowledgeBoxModel.getKnowledge());
}
}
public AbstractAlgorithmRunner(Parameters params, Graph... graphs) {
this.graphs = Arrays.asList(graphs);
this.params = params;
}
public AbstractAlgorithmRunner(Parameters params, KnowledgeBoxModel knowledgeBoxModel, Graph... graphs) {
this.graphs = Arrays.asList(graphs);
this.params = params;
if (knowledgeBoxModel != null) {
getParams().set("knowledge", knowledgeBoxModel.getKnowledge());
}
}
public AbstractAlgorithmRunner(IndependenceFactsModel model,
Parameters params, KnowledgeBoxModel knowledgeBoxModel) {
if (model == null) {
throw new NullPointerException();
}
if (params == null) {
throw new NullPointerException();
}
this.params = params;
DataModel dataSource = model.getFacts();
if (knowledgeBoxModel != null) {
getParams().set("knowledge", knowledgeBoxModel.getKnowledge());
}
List names = dataSource.getVariableNames();
transferVarNamesToParams(names);
this.dataModel = dataSource;
}
public AbstractAlgorithmRunner(Graph graph, Parameters params,
KnowledgeBoxModel knowledgeBoxModel, IndependenceFacts facts) {
this(graph, params);
if (knowledgeBoxModel != null) {
getParams().set("knowledge", knowledgeBoxModel.getKnowledge());
}
if (facts != null) {
getParams().set("independenceFacts", facts);
}
}
//============================PUBLIC METHODS==========================//
public final Graph getResultGraph() {
return this.resultGraph;
}
/**
* By default, algorithm do not support knowledge. Those that do will
* speak up.
*/
public boolean supportsKnowledge() {
return false;
}
public ImpliedOrientation getMeekRules() {
return null;
}
public void setExternalGraph(Graph graph) {
this.externalGraph = graph;
}
public Graph getExternalGraph() {
return this.externalGraph;
}
@Override
public abstract String getAlgorithmName();
public final Graph getSourceGraph() {
return this.sourceGraph;
}
public final DataModel getDataModel() {
if (this.dataWrapper != null) {
DataModelList dataModelList = this.dataWrapper.getDataModelList();
if (dataModelList.size() == 1) {
return dataModelList.get(0);
} else {
return dataModelList;
}
} else if (this.dataModel != null) {
return this.dataModel;
} else {
// Do not throw an exception here!
return null;
}
}
final DataModelList getDataModelList() {
if (this.dataWrapper == null) return null;
return this.dataWrapper.getDataModelList();
}
public final void setResultGraph(Graph resultGraph) {
this.resultGraph = resultGraph;
}
public final Parameters getParams() {
return this.params;
}
public Object getResettableParams() {
return this.getParams();
}
public void resetParams(Object params) {
this.params = (Parameters) params;
}
//===========================PRIVATE METHODS==========================//
/**
* Find the dataModel model. (If it's a list, take the one that's
* selected.)
*/
private DataModel getSelectedDataModel(DataWrapper dataWrapper) {
DataModelList dataModelList = dataWrapper.getDataModelList();
if (dataModelList.size() > 1) {
return dataModelList;
}
DataModel dataModel = dataWrapper.getSelectedDataModel();
if (dataModel instanceof DataSet) {
DataSet dataSet = (DataSet) dataModel;
if (dataSet.isDiscrete()) {
return dataSet;
} else if (dataSet.isContinuous()) {
return dataSet;
} else if (dataSet.isMixed()) {
return dataSet;
}
throw new IllegalArgumentException("" +
"This data set contains a mixture of discrete and continuous " +
"
columns; there are no algorithm in Tetrad currently to " +
"
search over such data sets." + "");
} else if (dataModel instanceof ICovarianceMatrix) {
return dataModel;
} else if (dataModel instanceof TimeSeriesData) {
return dataModel;
}
throw new IllegalArgumentException(
"Unexpected dataModel source: " + dataModel);
}
private List measuredNames(Graph graph) {
List names = new ArrayList<>();
for (Node node : graph.getNodes()) {
if (node.getNodeType() == NodeType.MEASURED) {
names.add(node.getName());
}
}
return names;
}
private void transferVarNamesToParams(List names) {
getParams().set("varNames", names);
}
/**
* Adds semantic checks to the default deserialization method. This method
* must have the standard signature for a readObject method, and the body of
* the method must begin with "s.defaultReadObject();". Other than that, any
* semantic checks can be specified and do not need to stay the same from
* version to version. A readObject method of this form may be added to any
* class, even if Tetrad sessions were previously saved out using a version
* of the class that didn't include it. (That's what the
* "s.defaultReadObject();" is for. See J. Bloch, Effective Java, for help.
*/
private void readObject(ObjectInputStream s)
throws IOException, ClassNotFoundException {
s.defaultReadObject();
}
public String getName() {
return this.name;
}
public void setName(String name) {
this.name = name;
}
public List getGraphs() {
return this.graphs;
}
@Override
public Map getParamSettings() {
this.paramSettings.put("Algorithm", getAlgorithmName());
return this.paramSettings;
}
public Map getAllParamSettings() {
return this.allParamSettings;
}
public void setAllParamSettings(Map allParamSettings) {
this.allParamSettings = allParamSettings;
}
}
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